Recombination Rate and Selection Strength in HIV Intra-patient Evolution

Journal Article · 29 January 2010 · PLoS Computational Biology (Online)

DOI: https://doi.org/10.1371/journal.pcbi.1000660 · OSTI ID:1627196

Neher, Richard A. ^[1]; Leitner, Thomas ^[2]

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1. Univ. of California, Santa Barbara, CA (United States); DOE/OSTI
2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

The evolutionary dynamics of HIV during the chronic phase of infection is driven by the host immune response and by selective pressures exerted through drug treatment. To understand and model the evolution of HIV quantitatively, the parameters governing genetic diversification and the strength of selection need to be known. While mutation rates can be measured in single replication cycles, the relevant effective recombination rate depends on the probability of coinfection of a cell with more than one virus and can only be inferred from population data. However, most population genetic estimators for recombination rates assume absence of selection and are hence of limited applicability to HIV, since positive and purifying selection are important in HIV evolution. Yet, little is known about the distribution of selection differentials between individual viruses and the impact of single polymorphisms on viral fitness. Here, we estimate the rate of recombination and the distribution of selection coefficients from time series sequence data tracking the evolution of HIV within single patients. By examining temporal changes in the genetic composition of the population, we estimate the effective recombination to be ρ = 1.4±0.6×10^–5 recombinations per site and generation. Furthermore, we provide evidence that the selection coefficients of at least 15% of the observed non-synonymous polymorphisms exceed 0.8% per generation. These results provide a basis for a more detailed understanding of the evolution of HIV. A particularly interesting case is evolution in response to drug treatment, where recombination can facilitate the rapid acquisition of multiple resistance mutations. With the methods developed here, more precise and more detailed studies will be possible as soon as data with higher time resolution and greater sample sizes are available.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: National Science Foundation (NSF); USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC), Biological and Environmental Research (BER). Biological Systems Science Division
• Grant/Contract Number: AC52-06NA25396
• OSTI ID: 1627196
• Journal Information: PLoS Computational Biology (Online), Journal Name: PLoS Computational Biology (Online) Journal Issue: 1 Vol. 6; ISSN 1553-7358
• Publisher: Public Library of ScienceCopyright Statement
• Country of Publication: United States
• Language: English